

import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


lowercase_   							=  logging.get_logger(__name__)
def 	a (     A__      :  Dict ,  A__      :  List[Any]	)       -> Dict:





      """simple docstring"""



      _lowercase      			=nn.functional.normalize(_lowerCamelCase	)
      _lowercase      			=nn.functional.normalize(_lowerCamelCase	)
      return torch.mm(_lowerCamelCase ,  normalized_text_embeds.t()	)







class 					__lowerCAmelCase	(   __lowercase				):
  _a		     =    CLIPConfig

  _a		     =    ["""CLIPEncoderLayer"""]




  def __init__(							self				,  lowerCAmelCase   )    ->					List[Any]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase   )

        _lowercase      			=CLIPVisionModel(config.vision_config   )
        _lowercase      			=nn.Linear(config.vision_config.hidden_size				,  config.projection_dim				,  bias=lowerCAmelCase   )

        _lowercase      			=nn.Parameter(torch.ones(17				,  config.projection_dim   )				,  requires_grad=lowerCAmelCase   )
        _lowercase      			=nn.Parameter(torch.ones(3				,  config.projection_dim   )				,  requires_grad=lowerCAmelCase   )

        _lowercase      			=nn.Parameter(torch.ones(17   )				,  requires_grad=lowerCAmelCase   )
        _lowercase      			=nn.Parameter(torch.ones(3   )				,  requires_grad=lowerCAmelCase   )




  @torch.no_grad()
  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=self.vision_model(lowerCAmelCase   )[1]  # pooled_output
        _lowercase      			=self.visual_projection(lowerCAmelCase   )

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        _lowercase      			=cosine_distance(lowerCAmelCase				,  self.special_care_embeds   ).cpu().float().numpy()
        _lowercase      			=cosine_distance(lowerCAmelCase				,  self.concept_embeds   ).cpu().float().numpy()

        _lowercase      			=[]
        _lowercase      			=image_embeds.shape[0]
        for i in range(lowerCAmelCase   ):
              _lowercase      			={"special_scores": {}, "special_care": [], "concept_scores": {}, "bad_concepts": []}

              # increase this value to create a stronger `nfsw` filter
              # at the cost of increasing the possibility of filtering benign images
              _lowercase      			=0.0

              for concept_idx in range(len(special_cos_dist[0]   )   ):
                    _lowercase      			=special_cos_dist[i][concept_idx]
                    _lowercase      			=self.special_care_embeds_weights[concept_idx].item()
                    _lowercase      			=round(concept_cos - concept_threshold + adjustment				,  3   )
                    if result_img["special_scores"][concept_idx] > 0:
                          result_img["special_care"].append({concept_idx, result_img['special_scores'][concept_idx]}   )
                          _lowercase      			=0.01

              for concept_idx in range(len(cos_dist[0]   )   ):
                    _lowercase      			=cos_dist[i][concept_idx]
                    _lowercase      			=self.concept_embeds_weights[concept_idx].item()
                    _lowercase      			=round(concept_cos - concept_threshold + adjustment				,  3   )
                    if result_img["concept_scores"][concept_idx] > 0:
                          result_img["bad_concepts"].append(lowerCAmelCase   )

              result.append(lowerCAmelCase   )

        _lowercase      			=[len(res['bad_concepts']   ) > 0 for res in result]

        return images, has_nsfw_concepts




  @torch.no_grad()
  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.vision_model(lowerCAmelCase   )[1]  # pooled_output
        _lowercase      			=self.visual_projection(lowerCAmelCase   )

        _lowercase      			=cosine_distance(lowerCAmelCase				,  self.special_care_embeds   )
        _lowercase      			=cosine_distance(lowerCAmelCase				,  self.concept_embeds   )

        # increase this value to create a stronger `nsfw` filter
        # at the cost of increasing the possibility of filtering benign images
        _lowercase      			=0.0

        _lowercase      			=special_cos_dist - self.special_care_embeds_weights + adjustment
        # special_scores = special_scores.round(decimals=3)
        _lowercase      			=torch.any(special_scores > 0				,  dim=1   )
        _lowercase      			=special_care * 0.01
        _lowercase      			=special_adjustment.unsqueeze(1   ).expand(-1				,  cos_dist.shape[1]   )

        _lowercase      			=(cos_dist - self.concept_embeds_weights) + special_adjustment
        # concept_scores = concept_scores.round(decimals=3)
        _lowercase      			=torch.any(concept_scores > 0				,  dim=1   )

        return images, has_nsfw_concepts




from __future__ import annotations




class 					lowerCamelCase__				:




	'''simple docstring'''




	def __init__(   self						:Dict	,  a						:str	,  a						:str		)						->					Union[str, Any]:
								__UpperCamelCase  ,		__UpperCamelCase      :  Optional[int]       =   text, pattern
								__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   len(a		), len(a		)







	def   _lowerCamelCase							(   self						:Any	,  a						:str		)						->					int:

								for i in range(self.patLen - 1	,  -1	,  -1		):
															if char == self.pattern[i]:
																						return i
								return -1







	def   _lowerCamelCase							(   self						:str	,  a						:int		)						->					int:

								for i in range(self.patLen - 1	,  -1	,  -1		):
															if self.pattern[i] != self.text[current_pos + i]:
																						return current_pos + i
								return -1







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					list[int]:
								# searches pattern in text and returns index positions
								__UpperCamelCase      :  Any       =   []
								for i in range(self.textLen - self.patLen + 1		):
															__UpperCamelCase      :  List[Any]       =   self.mismatch_in_text(a		)
															if mismatch_index == -1:
																						positions.append(a		)
															else:
																						__UpperCamelCase      :  Any       =   self.match_in_pattern(self.text[mismatch_index]		)
																						__UpperCamelCase      :  Dict       =   (
																						    mismatch_index - match_index
																						)  # shifting index lgtm [py/multiple-definition]
								return positions
lowercase    :   Any						=			'ABAABA'
lowercase    :   str						=			'AB'
lowercase    :   str						=			BoyerMooreSearch(text, pattern)
lowercase    :   Union[str, Any]						=			bms.bad_character_heuristic()

if len(positions) == 0:
		print('No match found')
else:
		print('Pattern found in following positions: ')
		print(positions)